Neutrophil extracellular traps as a potential marker of systemic lupus erythematosus activity.

Background: The heterogeneity of systemic lupus erythematosus (SLE) poses a significant challenge in identifying biomarkers for assessing disease activity. Currently, there is a paucity of established biomarkers capable of evaluating SLE flares. This study aimed to identify novel biomarkers that exhibit improved diagnostic accuracy in assessing SLE activity.

An activatable "AIE + ESIPT" fluorescent probe for dual-imaging of lipid droplets and hydrogen peroxide in drug-induced liver injury model.

Background: Drug-induced liver injury (DILI) is one of the most common liver diseases. The crucial role of lipid droplets (LDs) and hydrogen peroxide (HO), two important biomarkers in the pathophysiology of DILI, has spurred considerable efforts to accurately visualize HO and LDs for elucidating their functions in the progression of DILI. However, construction of a single fluorescent probe that is able to simultaneously image HO and LDs dynamics remains to be a challenging task.

Can photonic heterostructures provably outperform single-material geometries?

Recent advances in photonic optimization have enabled calculation of performance bounds for a wide range of electromagnetic objectives, albeit restricted to single-material systems. Motivated by growing theoretical interest and fabrication advances, we present a framework to bound the performance of photonic heterostructures and apply it to investigate maximum absorption characteristics of multilayer films and compact, free-form multi-material scatterers. Limits predict trends seen in topology-optimized geometries - often coming within factors of two of specific designs - and may be utilized in conjunction with inverse designs to predict when heterostructures are expected to outperform their optimal single-material counterparts.

DYRK1A interacts with the tuberous sclerosis complex and promotes mTORC1 activity.

DYRK1A, a ubiquitously expressed kinase is linked to the dominant intellectual developmental disorder, microcephaly, and Down syndrome in humans. It regulates numerous cellular processes such as cell cycle, vesicle trafficking, and microtubule assembly. DYRK1A is a critical regulator of organ growth; however, how it regulates organ growth is not fully understood.

Rapid Concentration of Ga-68 and Proof-of-Concept Microscale Labeling of [Ga]Ga-PSMA-11 in a Droplet Reactor.

The radiometal gallium-68 (Ga-68) has garnered significant interest due to its convenient production via compact and widely available generators and the high performance of Ga-labeled compounds for positron-emission tomography (PET) imaging for cancer diagnosis and management of patients undergoing targeted radionuclide therapy. Given the short half life of Ga-68 (68 min), microfluidic-based radiosynthesis is a promising avenue to establish very rapid, efficient, and routine radiolabeling with Ga-68; however, the typical elution volume of Ga-68 from a generator (4-10 mL) is incompatible with the microliter reaction volumes of microfluidic devices. To bridge this gap, we developed a microscale cartridge-based approach to concentrate Ga-68.

Nanomedicines modulate the tumor immune microenvironment for cancer therapy.

Introduction: In recent years, the evolution of immunotherapy as a means to trigger a robust antitumor immune response has revolutionized cancer treatment. Despite its potential, the effectiveness of cancer immunotherapy is hindered by low response rates and significant systemic side effects. Nanotechnology emerges as a promising frontier in shaping the future of cancer immunotherapy.

Revolution of Medical Review: The Application of Meta-Analysis and Convolutional Neural Network-Natural Language Processing in Classifying the Literature for Head and Neck Cancer Radiotherapy.

This study explored the application of meta-analysis and convolutional neural network-natural language processing (CNN-NLP) technologies in classifying literature concerning radiotherapy for head and neck cancer. It aims to enhance both the efficiency and accuracy of literature reviews. By integrating statistical analysis with deep learning, this research successfully identified key studies related to the probability of normal tissue complications (NTCP) from a vast corpus of literature.

Improving Prediction of Complications Post-Proton Therapy in Lung Cancer Using Large Language Models and Meta-Analysis.

Purpose: This study enhances the efficiency of predicting complications in lung cancer patients receiving proton therapy by utilizing large language models (LLMs) and meta-analytical techniques for literature quality assessment.

Materials And Methods: We integrated systematic reviews with LLM evaluations, sourcing studies from Web of Science, PubMed, and Scopus, managed via EndNote X20. Inclusion and exclusion criteria ensured literature relevance.

Liposome biodistribution mapping with X-ray fluorescence imaging.

Lipid-based nanoparticles are organic nanostructures constituted of phospholipids and cholesterol, displaying high biocompatibility. They have been demonstrated as effective nanocarriers for drug delivery and targeting. Mapping liposome distribution is crucial as it enables a precise understanding of delivery kinetics, tissue targeting efficiency, and potential off-target effects.

A mini review of plan quality and secondary cancer risk in CyberKnife M6 radiosurgery for benign intracranial tumors.

With advancements in medical technology, stereotactic radiosurgery (SRS) has become an essential option for treating benign intracranial tumors. Due to its minimal side effects and high local control rate, SRS is widely applied. This paper evaluates the plan quality and secondary cancer risk (SCR) in patients with benign intracranial tumors treated with the CyberKnife M6 system.

Gastrointestinal stromal tumours: incidence, recurrence and mortality. A decade of patients from a New Zealand tertiary surgical centre.

Background: Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal tumours of the gastrointestinal tract. The New Zealand (NZ) population incidence has not previously been documented nor has the potential effect of ethnicity been reviewed. We furthermore wanted to assess the difference between those undergoing a wedge resection versus a more extensive operation which we hypothesised would correlate with recurrence and mortality.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.

Purpose: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).

Materials And Methods: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information.

Discovery of a mu-opioid receptor modulator that in combination with morphinan antagonists induces analgesia.

Morphinan antagonists, which block opioid effects at mu-opioid receptors, have been studied for their analgesic potential. Previous studies have suggested that these antagonists elicit analgesia with fewer adverse effects in the presence of the mutant mu-opioid receptor (MOR; S196A). However, introducing a mutant receptor for medical applications represents significant challenges.

Integration of molecular docking and molecular dynamics simulations with subtractive proteomics approach to identify the novel drug targets and their inhibitors in Streptococcus gallolyticus.

Streptococcus gallolyticus (Sg) is a non-motile, gram-positive bacterium that causes infective endocarditis (inflammation of the heart lining). Because Sg has gained resistance to existing antibiotics and there is currently no drug available, developing effective anti-Sg drugs is critical. This study combined core proteomics with a subtractive proteomics technique to identify potential therapeutic targets for Sg.

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

Purpose: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models.

Materials And Methods: Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters.

Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle.

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies.

Novel Anti-Viral Properties of the Herbal Extract of against Influenza A Virus.

Gu-Sui-Bu, the dried rhizome of , is a traditional Chinese herbal remedy with a significant history of treating osteoporosis and inflammatory conditions. However, its potential as an anti-influenza agent and its underlying mechanisms of action remain unexplored. To obtain a more potent extract from and gain insights into its mechanism of action against influenza A virus (IAV), we utilized a partitioning process involving organic solvents and water, resulting in the isolation of butanolic subfractions of the extract (DMBE).

Risks of maternal cardiopulmonary events associated with ritodrine for tocolysis: A national database linkage study in 1 831 564 pregnant women.

Objective: Real-world data on cardiopulmonary events among pregnant women receiving β-agonist therapy are scarce. In the present study, we aimed to examine the absolute and relative risks of maternal cardiopulmonary events associated with the use of β-agonist ritodrine during pregnancy.

Methods: By linking Taiwan's National Birth Certificate Application Database with National Health Insurance data, 1 831 564 pregnancies at ≥20 weeks' gestation were identified.

Upper gastrointestinal and hepatopancreaticobiliary surgery in New Zealand: Balancing the volume-outcome relationship with accessibility in a surgically low volume country.

Introduction: New Zealand has a population of only 5.5 million meaning that for many surgical procedures the country qualifies as a "low-volume center." However, the health system is well developed and required to provide complex surgical procedures that benchmark internationally against comparable countries.